Notes

Abstract:

I estimated size selectivity of bottom trawl sampling for black crappie Pomoxis nigromaculatus utilizing direct and indirect approaches. I used capture-recapture methods to directly measure the effects of fish size on catchability (q, the fraction of a fish stock collected with a given unit of fishing effort) at Lake Jeffords, Florida. Estimates of q were made for different length-groups roughly resembling age-classes 0 to 2 and fish 3+ (90-119, 120-149, 150-179, 180+ mm) by marking a subpopulation collected using three gear types (bottom trawls, hoopnets, and electrofishing). Recapture sampling with otter trawls occurred two weeks after marking events ended, allowing a direct estimate of q from recaptures of tagged fish from each gear and size class. Indirect estimates of selectivity were obtained with a population model applied to long-term data at four Florida lakes. I constructed age-structured models for each lake that predicted annual catches-at-age as a function of measured growth rates, a time series of recruitment anomalies, assumed survival rates, and unknown age/size selectivities. Selectivity parameters were estimated by fitting model predicted catches-at-age to a time series of bottom trawl catch-at-age using maximum likelihood. Direct measures of selectivity indicated catchability was highest for the 90-119 length-group and lowest for fish greater than or equal to 180 mm, with q declining by a factor of 2 or 3 for large fish relative to small fish. Model simulations from the age-structured indirect approach revealed dome-shaped selectivity patterns with relative selectivities peaking at age-1 for three of four lakes. Lake Johns was the only exception where age-0 fish was the most efficiently captured age-group when survival was low. Overall model trends indicated greater selectivity of younger fish (age-0 and age-1) to the gear followed by decreasing relative selectivity to older age-classes (age-2+). Trawl selectivity patterns suggested that otter trawls would be best for monitoring the abundance of small black crappie. My results indicate that adult black crappie will likely be underrepresented in bottom trawl samples, which would influence age structure and growth rate estimates and the effectiveness of this gear as an assessment tool for tracking adult crappie populations.

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Notes

Abstract:

I estimated size selectivity of bottom trawl sampling for black crappie Pomoxis nigromaculatus utilizing direct and indirect approaches. I used capture-recapture methods to directly measure the effects of fish size on catchability (q, the fraction of a fish stock collected with a given unit of fishing effort) at Lake Jeffords, Florida. Estimates of q were made for different length-groups roughly resembling age-classes 0 to 2 and fish 3+ (90-119, 120-149, 150-179, 180+ mm) by marking a subpopulation collected using three gear types (bottom trawls, hoopnets, and electrofishing). Recapture sampling with otter trawls occurred two weeks after marking events ended, allowing a direct estimate of q from recaptures of tagged fish from each gear and size class. Indirect estimates of selectivity were obtained with a population model applied to long-term data at four Florida lakes. I constructed age-structured models for each lake that predicted annual catches-at-age as a function of measured growth rates, a time series of recruitment anomalies, assumed survival rates, and unknown age/size selectivities. Selectivity parameters were estimated by fitting model predicted catches-at-age to a time series of bottom trawl catch-at-age using maximum likelihood. Direct measures of selectivity indicated catchability was highest for the 90-119 length-group and lowest for fish greater than or equal to 180 mm, with q declining by a factor of 2 or 3 for large fish relative to small fish. Model simulations from the age-structured indirect approach revealed dome-shaped selectivity patterns with relative selectivities peaking at age-1 for three of four lakes. Lake Johns was the only exception where age-0 fish was the most efficiently captured age-group when survival was low. Overall model trends indicated greater selectivity of younger fish (age-0 and age-1) to the gear followed by decreasing relative selectivity to older age-classes (age-2+). Trawl selectivity patterns suggested that otter trawls would be best for monitoring the abundance of small black crappie. My results indicate that adult black crappie will likely be underrepresented in bottom trawl samples, which would influence age structure and growth rate estimates and the effectiveness of this gear as an assessment tool for tracking adult crappie populations.

General Note:

In the series University of Florida Digital Collections.

General Note:

Includes vita.

Bibliography:

Includes bibliographical references.

Source of Description:

Description based on online resource; title from PDF title page.

Source of Description:

This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.

Beamesderfer, R. C., and B. E. Rieman. 1988. Size selectivity and bias in estimates of
population statistics of smallmouth bass, walleye, and northern squawfish in a Columbia
River Reservoir. North American Journal of Fisheries Management 8:505-510.

Diamond, S. L., L. B. Crowder, and L. G. Cowell. 1999. Catch and bycatch: The qualitative
effects of fisheries on population vital rates of Atlantic croaker. Transactions of the
North American Fisheries Society 128:1085-1105.

Pierce, R. B. and C. M. Tomcko. 2003. Variation in gill-net and angling catchability with
changing density of northern pike in a small Minnesota lake. Transactions of the
American Fisheries Society 132:771-779.

Pine III, W. E. 2000. Comparison of two otter trawls of different sizes for sampling black
crappies. North American Journal of Fisheries Management 20:819-821.

Wakefield, C. B., M. J. Moran, N. E. Tapp, and G. Jackson. 2007. Catchability and selectivity
of juvenile snapper and western butterfish from prawn trawling in a large marine
embayment in Western Australia. Fisheries Research 85:37-48.

Gregory Robert Binion was born on September 18, 1978, at a U.S. air force base in the

United Kingdom, to Mike and Peggy Binion. Shortly after, he moved and was raised in San

Antonio, Texas, with his older brother Pete. At a young age, he developed a passion for the

outdoors and enjoyed much of his time exploring the wide open spaces and beautiful country of

South Texas. He graduated from the University of Kentucky with a B.A. in political science in

December 2002. Shortly after graduation, he relocated to Florida to pursue an interest in

fisheries biology and management. In October 2003, he began to work as a fisheries technician

on various projects at the University of Florida and began his graduate work at the Department of

Fisheries and Aquatic Sciences at the University of Florida in January 2006.He will graduate

with a Master of Science in December 2007. His future plans include traveling, passing time

fishing and hunting, spending time with his family, and pursuing a career in fisheries

management.

PAGE 1

DIRECT AND INDIRECT ESTIMATES OF BLACK CRAPPIE SIZE SLECTIVITY TO OTTER TRAWLS By GREGORY R. BINION A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007 1

PAGE 2

2007 GREGORY R. BINION 2

PAGE 3

To my grandfather Ace who instilled in me at a young age a passion for fish. 3

PAGE 4

ACKNOWLEDGMENTS I would like to thank and acknowledge my supervisory committee members Dr. Bill Pine, Jim Estes, and Marty Hale for their encourag ement and support, and especially my committee chair, Dr. Mike Allen, for his mentorship and direction throughout my research. I would also like to thank various members of the Allen lab for their help in field collection and processing; Galen Kaufman, Jason Dotson, Kevin Johnson, Christian Barrientos, Erika Thompson, Aaron Bunch, Melissa Woods-Jackson and Drew Dutterer. I would like to acknowledge various members of the Florida Fi sh and Wildlife Conservation Commission (Eric Nagid, Travis Tuten, Will Strong, Bill Johnson, and Ja nice Kerns) for collaboration in research. A special thanks goes to office mates Matt Cata lano and Mark Rogers whom encouraged and supported my development as a student, mentori ng and answering questions when I encountered problems. Finally, I want to thank my loving wife, parents and grandparents for their endless encouragement, love, and support. 4

LIST OF TABLES Table page 3-1 Summary of Lake charact eristics for study locations........................................................29 3-2 Summary of CPUE-at-age (fish/min) data showing the mean, minimum, and maximum values for each age and lake and combined across lakes.................................30 6

Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science DIRECT AND INDIRECT ESTIMATES OF BLACK CRAPPIE SIZE SLECECTIVITY TO OTTER TRAWLS By GREGORY R. BINION December 2007 Chair: Mike Allen Major: Fisheries a nd Aquatic Sciences I estimated size selectivity of bottom tr awl sampling for black crappie Pomoxis nigromaculatus utilizing direct a nd indirect approaches. I used capture-recapture methods to directly measure the effects of fish size on catchability (q, the fr action of a fish stock collected with a given unit of fishing effo rt) at Lake Jeffords, Florida. Estimates of q were made for different length-groups roughly resembling ageclasses 0 to 2 and fish 3+ (90-119, 120-149, 150179, 180+ mm) by marking a subpopulation collected using three gear types (bottom trawls, hoopnets, and electrofishing). Recapture sampling with otter trawls occu rred two weeks after marking events ended, allowing a direct estimate of q from recaptures of tagged fish from each gear and size class. Indirect estimates of selectivity were obtained with a population model applied to long-term data at four Florida lakes. I constructed age-structured models for each lake that predicted annual catches-at-a ge as a function of measured gr owth rates, a time series of recruitment anomalies, assumed survival rates, and unknown age/size selec tivities. Selectivity parameters were estimated by fitting model predicte d catches-at-age to a time series of bottom trawl catch-at-age using maximum likelihood. Direct measures of selectivity indicated catchability was highest for the 90119 length-group and lowest for fish greater than or equal to 180 mm, with q declining by a factor of 2 or 3 for large fish relative to small fish. Model 8

PAGE 9

9 simulations from the age-structured indirect appr oach revealed dome-shaped selectivity patterns with relative selectivities peaking at age-1 for three of four lakes. Lake Johns was the only exception where age-0 fish was the most efficien tly captured age-group when survival was low. Overall model trends indicated gr eater selectivity of younger fish (age-0 and age-1) to the gear followed by decreasing relative selectivity to ol der age-classes (age-2+). Trawl selectivity patterns suggested that otter traw ls would be best for monitori ng the abundance of small black crappie. My results indicate that adult black crappie will likely be unde rrepresented in bottom trawl samples, which would influence age st ructure and growth rate estimates and the effectiveness of this gear as an assessment t ool for tracking adult cra ppie populations.

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CHAPTER 1 INTRODUCTION Effective management of fishery resources is dependent on the quality of information available for decisions. To deve lop optimal management strategies, biologists must be confident that sampling reliably tracks popul ation metrics. These strategi es are often reliant on precise estimates of some metric of population si ze and its correspondi ng level of production (biomass/numbers). Gear selectivity can influe nce precision and accuracy of these measures (Hilborn and Walters 1992). Samp les collected from many fish populations with a variety of gears often dont accurate ly describe the true age and size structure of the target population. Therefore, obtaining an abundance index that reflects the actual age/size composition of a population allows managers to monitor population trends such as recruitment, growth, and mortality and evaluate populati on responses to management po licies (e.g., size limits) (Hilborn and Walters 1992). When evaluating gear types, an important di stinction between gear selectivity and gear efficiency must be delineated. Gear selectivity is defined as the composition of a sample relative to the true population metric (e.g. size, age, growth rate), and re lative selectivity refers to the effectiveness of a sampling gear to capture a part icular size or species of fish relative to its efficiency at capture of other sizes or species (Hubert 1996). In cont rast, gear efficiency describes the magnitude of effort requir ed to catch adequate sample sizes. Gear selectivity patterns are commonly attrib uted to intrinsic f actors like fish size (Beamesderfer and Rieman 1988, Myers and Hoenig 1997, Wakefield et al. 2007), fish density (McInerny and Cross 2000, Rogers et al. 2003), species (Laarman and Ryckman 1982, Sammons et al. 2002), sex (Jagielo 1999) behavioral patterns (Reynol ds 1996, Jagielo 1999), and habitat preferences (Jacobson et al. 2001) as well as extrinsic factors su ch as seasonal variation (Pope 10

PAGE 11

and Willis 1996), environmental conditions or ch aracteristics (Hayes et al. 1996; McInerny and Cross 2000), diel variation (Paragamian 1989, Dumont and Dennis 1997), gear construction (ONeil and Kynoch 1996, Lok et al. 1997, Farmer et al. 1998), gear type (Kraft and Johnson 1992, Jackson and Noble 1995, Otway et al. 1996), and sampling crew expertise. Estimates of gear selectivity are important for fish stock assessment. Estimates of selectivity allow managers to assess populati on composition based on samples which may not represent the true population, and estimates of selectivity provid e information on aspects of the population which is not readily observable. Adjusting for selectivity allows managers to obtain a more accurate abundance index for the age and size structure of a stock because many samples do not adequately represent the true population age or size structure. This enhances the ability of managers to draw inferences about stock trends like recruitment, growt h, and mortality (Hilborn and Walters 1992). Gear selectivities are commonly used to dete rmine the effects of fishing on the size and age composition of a fishery and are commonly us ed in assessment models to link size/age structure of catch data to the size/age structure of the fish pop ulation (Walters and Martell 2004; Taylor et al. 2005). Such models are required to predict effect s of different harvest rates, calculate biological reference points like spaw ning potential ratio ( SPR), and determining appropriate levels of sustainable yield for a fishery (Maunder 2002). Thus, quantifying gear selectivity allows biologists to adjust abundance indices to represent the true size/age composition which guides future management actions. Measurements of the selective properties of fishing gears are often made utilizing direct and indirect methods (Pollock et al. 1990; Walters and Martel l 2004). Direct methods involve comparing catch composition against a known populati on structure. The most direct method for 11

PAGE 12

estimating selectivity is a mark recapture experiment creating a known population, then calculating the proportion of fish caught by the g ear in a given length category from the marked subpopulation (Hamley and Regier 1973; Myers and Hoenig 1997; McInerny and Cross 2006). Accurate estimation of selectiv ity using capture-recapture methods requires several assumptions including: (1) the population of inte rest is closed to additions and deletions. That is, recruitment, natural mortality, immigration, and emigration must be minimal, (2) tags are not lost or go undetected, and (3) equal capture probability (i.e. no capture heterogeneity and/or trap response) (Pollock et al. 1990). Unlike dire ct methods, indirect measures of selectivity require no prior knowledge about the age composition of a population. If catch-at-age data from the commercial or recreational sectors are available, age stru ctured population models like virtual population analysis (VPA) can estimate the age/size selectiv e properties of the fish ing gear used. Other approaches incorporate the catch ra tes of various sizes of fish fr om different gear types and/or mesh size to compare relative gear selectivity be tween gears, but such studies do not identify the true selectivity of e ither gear (e.g., Boxrucker and Plosky 1989; Miranda et al. 1992; Millar and Holst 1997). Indirect or relative measures of abundance such as catch per unit effort (CPUE) are commonly used by managers to assess and analyze trends in fish population abundance. In order for CPUE to directly index population abundance, the relation ship between catch rate and abundance must be: A N q f C (1) where C = catch, f = fishing effort, q = catchability coefficient (the fraction of population removed per unit of effort), N = fish abundance and A = area occupied by stock (Ricker 1975). 12

PAGE 13

This equation infers a linear relationship be tween CPUE and abundance with a constant slope q, which is often not the case. Catch per effort is a function of two factors: catchability and fish density (Hilborn and Walters 1992; Arreguin-Sanchez 1996) Therefore, variability in q causes variability in CPUE that is not related to populati on size, so catch statistics should be adjusted to account for variation in catchability. Because catchability is a function of selectivity, it is an important parameter when using CPUE to index abundance. Furthermore, when catchability coefficients are apportioned by age/size classes, the estimated co efficients are actual ag e/size gear selectivity estimates. Like selectivity, catchability differs with a wi de range of factors including fish age (Pierce and Tomcko 2003), fish size (Bayley and Au sten 2002; McInerny and Cross 2006), species (Bayley and Austen 2002; Schoenebeck and Hans en 2005), fish density (Peterman and Steer 1981; Rogers et al. 2003), sample gear type (Hansen et al. 2000; Pierce and Tomcko 2003), environmental conditions during sampling (B ayley and Austen 2002), and sampling season (Schoenebeck and Hansen 2005; McInerny and Cross 2006). Nielson (1983) found catchability from otter trawls to be similar across age-cl asses of adult yellow perch. Pierce and Tomcko (2003) showed q of northern pike to vary with age in gill-nets. McInerny and Cross (2006) quantified the effects of size, se ason, and density of black crappie on trap-net catchability. They found q increased with fish size, and catchability was higher in spring than fall. Catchability also varied with density as both increased and decr eased values of catchability were observed for different length-groups and sampling periods Bayley and Austen (2002) provided a comprehensive evaluation of electrofishing q estimates for different fish species, sizes, and seasons under variable environmental conditions. Overall, knowledge of q can allow managers 13

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to adjust abundance indices and estimate absolu te abundances, estimate gear selectivity patterns, identify seasonal and/or environmental biases as sociated with sample gears, and aid in the selection of appropriate gears to maximize management objectives. Black crappie support one of the most popular sport fisheries in North America often ranking first or second among angler preference, but can be difficult to manage. Sampling crappies to accurately describe rate functions (such as growth and mortality), abundance and size structure is often demanding re quiring much effort. Indexing black crappie abundance and size structure is challenging due to differences in ge ar performance and selec tivity patterns. In the Midwest, trap nets have been useful in co llecting large samples of crappie of all sizes (Gablehouse 1984; Colvin and Vasey 1986; Boxr ucker and Ploskey 1989), but true gear selectivity has rarely been measured (but s ee McInerny and Cross 2006). Conversely, trap nets in some southeastern systems collected young fish but few adults (Sammons and Bettoli 1998; Maceina et al. 1998). Miranda and Dorr (2000) quantified the size selectiv e effects of crappie angling in five southeastern systems and reporte d dome-shaped selectivity for crappie vulnerable to angling (size range: 20.0 to 39.8 cm) with sma ller and larger sized cr appie less susceptible than intermediate sizes indicating differences in catchabili ty and thus, exploitation. Trap net efficiency and relative selectivity ha s been evaluated and compared to other gears in numerous studies to determine which methods of capture are most effective (McInerny 1989; Boxrucker and Ploskey 1989; Miranda et al. 1992; St. John and Black 2004). McInerny (1989) found trap nets were the most effective and cost-efficient gear deployed for sampling black crappie populations at Lake Wylie, North Carolina. Miranda et al. (1992) reported a higher catch per effort with trap nets compared to electr ofishing in the spring and rotenone sampling in the summer for four Mississippi wate rs. Boxrucker and Ploskey ( 1989) revealed greater sampling 14

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efficiency and less variation in catch per effort with trap nets when co mpared to electrofishing and gillnetting. Thus, trap nets have provided us eful data in some cases, but the selectivity of trap nets relative to the population size/age has seldom been measured. Otter trawls have received far less attention than other capture methods to index crappie abundance. In Florida waters, otter trawls ha ve proven to be successful at capturing black crappies (Schramm et al. 1985; Allen et al. 1999 ; Pine 2000). Allen et al. (1999) compared the relative efficiency of trap nets versus otter traw ls for sampling black crappie in two Florida lakes and reported that trawl sampling was superior to trap nets based on the size range of fish collected, accuracy of abundance estimates, required sampling effort, and expenditures associated with gear. Pine (2000) compared the relative selectivity of two different sized bottom trawls and found a smaller trawl was more effectiv e at collecting juvenile black crappie than a larger trawl. Despite the importance of black crappie in Florida and the popularity of bottom trawls for sampling crappie populations, trawl se lectivity of black cr appie relative to the population has not been measured. Thus, in order for biologists to utilize trawl catch data for management it is important to understand the sele ctive properties of the gear relative to the population. This will enhance the ability of managers to use trawl CPUE as an index of abundance as well as length and age frequency information to describe the size/age structure of black crappie populations. My objectives were to (1) estimate size-spec ific catchability (q) of black crappie collected with otte r trawls (2) estimate relative age/size-specific selectivity of bottom trawl gears and (3) use those selectivity patte rns to evaluate the utility of otter trawls as an assessment gear for black crappie for Florida lakes. 15

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CHAPTER 2 METHODS Direct Measure of Selectivity Capture-recapture sampling took place at La ke Jeffords, Florida during January 2007. Lake Jeffords is a 65 hectare, mesotrophic (Pin e 2000) system located in Alachua County, North Central Florida (Figure 1). I selected Jeffords b ecause I felt I could adequately sample the entire lake (i.e. sample all available habitat types) and create a la rge enough marked subpopulation to obtain reliable catchabi lity estimates. Mark-recapture methods were used to create a tagged population using three gear types. Marking took place over a 10 day period in Janu ary 2007, with electrofishing gear sampled on day 1, otter trawls sampled on days 1, 2, and 3, and hoopnets sampled on days 7 10. I sampled with three gears during the marking event to ensure all available habitat t ypes of the lake were sampled. The recapture event took place over a two day period with bottom trawls two weeks after the first marking day. I only used bottom trawls during the recaptu re period, which allowed estimation of trawl size selectivity based on my known tagged population. The perimeter of the lake was electrofished at both events to ensure fish had not moved into the shallow littoral zone where it is not possible to effectively trawl. Ca ptured fish from all trawls were divided into subgroups by length. This division allowed estimation of q by size, providing a measure of actual trawl size se lectivity. The length-groups (mm) roughly resembled ages 0 (90-119), 1 (120-149), 2 (150-179) and adult fish three or ol der (180+). Abundance estimates were obtained using a Lincoln-Peterson estimator (due to closed system and 2-stage mark-capture sampling event) and the proportions of marked fish were calculated as the number of fish caught in the recapture divided by the abundance estimate. Al l black crappie captured in the field during marking were measured for total length (TL) to nearest (mm) and pelvic fin clipped. Since only 16

PAGE 17

two weeks passed from mark to recap events and fish were fin clipped instead of using conventional tag types like a T-bar tag, I assumed tag loss to be negligible. All black crappie captured during the recapture wher e measured for total length to nearest (mm) and checked for fin clips. Bottom trawls were pulled from a 7-m boat pow ered with a 70 hp outboard in all areas of the lake except in the shallow littoral zone to avoid fouling by vegetation. Effort was constant throughout the study at three minutes per trawl. The trawl net consisted of a 4.88-m long body and 4.6-m mouth and the body is constructed with 38.1 mm stretch mesh and 31.8 mm stretch mesh in the cod end (Allen et al. 1999). Under to w, the mouth of the trawl is spread open with floats (25 x 50 mm) that are secured to the head rope of the trawl mout h. The sweep, or chain line, was attached to the footrope of the ne t. Wooden doors (38.1 x 76.2 cm) were secured to 146 cm leglines and a 15.3-m trawl bridle. The we ighted doors served to open the trawl mouth and allowed the net to sample near the bottom. Modified hoop nets were deployed in the middl e of the lake at various sites. Hoop nets consisted of four similar-sized fiber-glass hoops either 0.9, 1.2 or 1.5m in diameter and covered with 5.1cm stretch nylon mesh webbing. A 23-m lead was used to connect two nets, which would direct fish toward a hoop net as they tr aveled along the lead. All hoop nets were set during the day, fished for 48 hours, and retrieved. Hoop nets were only used for capture sampling event. Electrofishing was conducted with a Smith-R oot model SR18 electr ofisher, equipped with a Smith-Root 9.0 GPP pulsator powered by a 9,000 Watt generator. Approximately 7 amps of DC current were produced at 120 pulses pe r second. The entire shoreline perimeter was sampled (as described above) with an experienced crew of one nette r and one boat operator. 17

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I estimated tagging mortality, defined as morta lity from capture, handling, and tagging for each size-group to adjust the size of our mark ed population available for recapture. A subsample of marked fish were held in aerated bait tanks and placed in holding pens as replicates (n = 8) for 24 hours to estimate associated taggi ng mortality for different length-groups. Holding pens were constructed out of pvc pipe which co nsisted of a rectangular frame that measured 3.0 m length by 1.25 m width. The body of the holding nets consisted of 19.3 mm stretch mesh webbing that extended to a depth of 1.1 m. The observed mortality rates for each size-group and holding pen were randomly re-sampled with re placement using a bootstrap to create 1,000 Monte Carlo estimates (Haddon 2001). The 95% confiden ce intervals were calculated at the 2.5 and 97.5 percentiles using the means of the resample from the bootstrap. I used maximum likelihood methods to estimate how q varied with fish size. The Poisson log likelihood function was appropriate and indicated as ))ln()()()|(lni i i iPOPqOL (2) where Pi = (number available for recapture in size-group i q effort) and Oi = (number of observed recaptures in size-group i ). Catchability for each length-group was estimated by maximizing the negative likelihood function (i.e. minimize the differences between the observed and expected recaptures). Parameters for the model included survival from marking ( S) = (1 observed tagging mortality), number availa ble for recapture = (number marked S), and q, the fraction of population caught per unit of effort. To describe the uncertainty in q estimates, I constructed likelihood profiles for each length-group. These profiles were probability dist ributions (i.e. p-values) for the parameters. The 95% confidence intervals for each parameter were calculated using Wilks likelihood ratio test statistic (Pawitan 2001) equal to 18

PAGE 19

)( )( ln*2 L hatL W (3) where W = Wilks statistic, L ( hat ) = log likelihood at MLE, and L ( ) = log likelihood at some value less than the maximum likelihood estimat e (MLE). Wilks stat istic conforms to a Chi-square distribution with one degree of freedom (Pawitan 2001). Indirect Measure of Selectivity Black crappie populations in Florida lakes have been sampled with otter trawls over the last two to three decades. I used a long-term da tabase from four lakes to estimate the size/age selectivity of bottom trawls us ing an age-structured population modeling approach. In this context, selectivity was defined as differences in re lative fish susceptibility due to size/age. The model was used to estimate selectivity at ag e by comparing observed and model-predicted catches-at-age using maximum likelihood estimation. Annual bottom trawl sample data were obtaine d from lakes Griffin, Johns, Lochloosa, and Okeechobee (Figure 2). The length of the data tim e series varied among lakes and ranged from five years for Lake Johns (2002-2006) to 20 years at Lake Okeechobee (1987-2006). All lakes exceed 1,500 ha with mean depths ranging from 1.8 m to 2.8 m and are classified as eutrophic or hypereutrophic (Florida Lakewatch 1999, 2001; Forsberg and Ryding 1980) (Table 1). Black crappie populations were sampled using a combination of fixed and/or random sites. Lakes Lochloosa and Johns were divided into 250m2 grids using ARC GIS software with buffers built in to avoid sampling in the vegetated littoral zone. Vegetation fouls the gear, reduces gear performance, and does not allow an accurate assessment of gear utility. At Lake Lochloosa, fixed and random sites were used throughout, wh ereas Lake Johns data were obtained from randomly generated fixed sites until years 2005 and 2006 where a combination of six fixed and six random sites were used. At Lake Griffin, 25 to 68 trawls were pulled based on a similar but 19

PAGE 20

slightly different SRS design (fixed sites to 2002 and random sites from 2003). One to 20 trawls were pulled at Lake Okeechobee on the north en d of the lake from 0.8 to 2.4 km offshore between Taylor Creek Lock (S-193) and Nubbin Slough Spillway (S-191) until 500 black crappies were captured. In 2005, due to a large drop in numbers likely attributed to the 2004 hurricane effects on the lake, the sampling fo cus changed from minimum numbers to minimum effort (~150 minutes). In lakes where a combination of fixed/random sites were used in a given year (Johns, Lochloosa) I tested for differences in mean CPUE and size st ructure to determine if fixed and random site samples could be pooled. Analysis of variance (ANOVA) indicated no significant site type or year*sit e type effects on mean CPUE fo r both lakes: Johns (P = 0.48, P = 0.44), Lochloosa (P = 0.86, P = 0.08). Differences in size structure by site type were evaluated using a Chi-square test and results indicated no si gnificant differences in si ze structures between fixed or random sites in any year. Based on th ese findings, I combined fixed and random sites for the analysis. There were some method differences among the lakes. Trawls were towed at a speed 2.0 2.5 m/s (1800-2000 RPM) with the exception of La ke Okeechobee where trawls were pulled at 1.0 m/s. Effort was constant throughout the st udy at three minutes per trawl with the exception of Lake Lochloosa where some trawls were pul led for five minutes, and Lake Okeechobee where trawls were pulled for 15 or 30 minute intervals (see Miller et al. 1990). Samples were collected during daylight hours from October December at Lakes Griffin, Johns and Lochloosa, and in January at Lake Okeechobee. The trawl net used at Lakes Griffin, Johns, and Lochloosa was the same as used for Lake Jeffords capture-recapture sampling (described above) which I will refer to as the standard trawl net. Lake Okeechobee trawl net was si milar in design and application 20

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but was larger, having a 10.7-m headrope, 32-mm square body mes h, and 25-mm square cod end mesh. All black crappie captured in the field were meas ured for total length (TL) to nearest (mm). Subsamples of five fish per one cm group were brought back to the laboratory for further analysis. However, age data were collected only every other year fr om 2001 to 2006 at Lake Lochloosa. At the laboratory, gender, total le ngth (TL) to nearest (mm) total body weight (TW, g) were determined and sagittal otoliths were re moved. Ages were determined in whole view by two independent readers as the number of opaque bands on sagittal otoliths. Otoliths from fish older than two years as well as any discrepa ncies on whole reads were sectioned along the dorsoventral plane before aging. Use of black crappie otoliths for aging in Florida has been validated by Schramm and Doerzbacher (1982). I used an age-structured popul ation model to estimate th e relative age/size-specific selectivity of black crappie to bottom trawls at each lake. The model predicted catch-at-age as a function of von Bertalanffy growth parameters, annual relative recruitment anomalies, an arbitrary number of intital recruits, assumed instantaneous rates of total mortality ( Zo = e-Zo= survival ( So ) to age-1, Z = e-Z = survival ( S) past age-1), and unknown age/size-specific selectivities (to be estimated). The numbers at age (Na,t) in a given year were estimated as ot tRRN *, 0 (4) otatSNN *1,1 ,1 (5) SNNtat*1,1 ,2 (6) where Rt are annual recruitment anomalies, Ro is the average annua l recruitment numbers (arbitrary value used to scale model), So is survival from age 0 to age 1, and S is annual survival for age-1+ fish. Different survival rates for ag e-0 relative to older fish were used because of 21

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expected lower survival for age-0 fi sh. Annual recruitment anomalies ( Rt) for each lake and year were estimated by dividing mean catch per e ffort (CPUE) of age-0 crappie in year t by the median age-0 CPUE across all years. This provide d an index of strong and weak year classes in the population model and was used as a basis for pred icting future catches-at-age with the trawls. This model allowed prediction of the relative numb ers of fish at each lake, age, and year based on the input parameters. From the numbers at age matrix, I predicted a catch-at-age matrix from a hypothesized selectivity schedule. Expected catch at age was calculated as atataSNC *,, (7) where Ca,t is the catch at age at time t and Sa are unknown selectivity at age parameters. Growth parameters were estimated using th e von Bertalanffy growth equation fitted to weighted mean length at age data obtained from age-length keys using the technique described by Devries and Frie (1996). The von Bertalanffy equation is ) 1(*))*((0taK aeLL (8) where La is length at age, L represents the average asymptotic length, K is the metabolic growth coefficient, a is fish age, and t0 is the age at zero length. A growth model was constructed for each lake by pooling annual length-a ge samples after determining that growth did not differ widely among years. The von Bertalanffy growth model was also used to link my agebased selectivities to length-based selectivities for each cohort. Because fish suffer higher rates of mortality ear ly in life than adults (Hjort 1914; Cushing 1975), my base model assumed different instantane ous rates of total mort ality for these two life stanzas: Z = 1.2 ( So = 0.30) for survival to age-1, and Z = 0.6 ( S = 0.54) for ages 1+. These rates served as a base for comparison to othe r simulations under vary ing assumptions for So and S I evaluated the sensitivity of selectivity estimat es to different survival rates by estimating 22

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selectivity parameters under different values of So and S Values of So ranged from 0.22 to 0.37 and S ranged from 0.45 to 0.67. Observed catch-at-age for each lake and y ear was estimated using age-length keys. Because age data were only collected at Lake Lo chloosa every other year, we used the previous and post years age data as the basis for an ag e length key (e.g., 2002 age structure was estimated using 2001 and 2003 age-length subsamples), and a pportioned fish to ages based on the existing length data. Observed catch-at-age for each year was standardized for sampling effort by dividing the catch-at-age by the total lake effort (trawl minutes) for that year. I used a multinomial log likelihood function to estimate selectivity at age by minimizing the differences between observed and expected (i.e., model-predicted) proportions of catch-atage using the Solver function in Excel. The multinomial log likelihood equation was at tata ataPOnSOL,, ,ln )|( ln (9) where n is the number of years model fit to catch data, Oa,t represents the observed proportion of catch at age a in year t and Pa,t is the predicted propor tion of catch at age a in year t I used a logit transformation on selectivity pa rameter estimates in the optimization routine to constrain selectivities between zero and one. When working with a parameter such as a probability that must be between zero and one the logit transformation allows parameter estimates to range from to The logit transformed selectivities were calculated as a aS S X 1 ln' (10) where X is the logit transformed selectivity at age. This logit transformation was used when solving for the point estimates of selectivit y, as well as for the lik elihood profiles (below). 23

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24 Parameter uncertainty was evaluated by cal culating 95% likelihood profile confidence intervals via a likelihood ratio test (Hilbor n and Mangel 1997) usi ng the likelihood profile function in Poptools for Excel ( www.cse.csiro.au/poptools/). Th e profile function allowed me to test alternative parameter estimates for all age-specific selectivities by holding one selectivity estimate constant, then iteratively solving fo r the maximum likelihood estimate by varying the remaining parameters and repeati ng with different valu es until the profile was constructed. The likelihood ratio test (LRT) was expr essed in terms of the differences in the deviance or twice the difference between the negative log-likelihoods (Hilborn and Mangel 1997). The deviance for each simulation was found by (11) max*2 LnL LnLrest where LnLrest is the likelihood value for rest ricted or nested model and LnLmax is the maximum likelihood value for full model. The like lihood ratio test is desc ribed by a Chi-square distribution with r degrees of freedom. The degrees of freedom were determined by the difference in the number of parameters estim ated between the mode ls (Hilborn and Mangel 1997). The LRT allowed comparisons of the probabilities for each selectivity estimate occurring relative to alternative para meter values (hypotheses).

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CHAPTER 3 RESULTS Direct Measure of Selectivity The number of marked fish and size range captu red varied by gear type. I marked 1,250 fish with bottom trawls (size range: 80 365 mm), 23 fish with hoopnets (160 312 mm), and 9 with electrofishing gear (123 318 mm). Recapture with bottom trawls netted 788 fish (88 304 mm), 54 of which were previously marked individuals. Based on my recapture rates and adjusting for differential tagging mortality, I ma rked approximately 0.065 % of the total black crappie population at Lake Jeffords. I marked 0.098 % of fish in group 90-119, 0.064 % of fish in 120-149, 0.073 % in 150-179, and 0.041 % of fish 180+. Tagging mortality for the smaller length groups (90-119, 120-149 mm) was much higher compared to the larger groups (150-179, 180+ mm) (Figure 3). The 90-119 and 120-149 lengthgroups experienced high tagging mortality at 68 and 36%, respectively. The 150-179 and 180+ groups experienced much lower tagging mortalit y rate averaging only 12 and 1%. Overall, tagging mortality was much higher for the two sma llest length-groups rela tive to larger fish. The likelihood profiles for each group indi cated an overall decreasing trend in q with increasing fish size (Figure 4). Results showed that the maximum likelihood estimates (MLEs) of q for the length-group 90-119 were 2 to 3 times higher than q estimates at larger sizes, assuming the calculated mean tagging mortality. The likelihood profiles revealed high uncertainty in all the estimates of q but higher uncertainty for the 90-119 mm size-group when compared to other groups. Figure 5 shows th e lower and upper 95% confidence bounds for the MLEs using mean mortality estimates. I evaluated how uncertainty from tagging mortality estimates would influence estimates of q The maximum likelihood estimates for q based on the lower, mean and upper tagging 25

PAGE 26

mortality rates are presented in Figure 6. Based on mean tagging mo rtality rates, maximum likelihood estimates for length-group 90-119 were approximately twice the MLE values of groups 120-149, 150-179 and three times as high as length-group 180+. Differences in the MLE estimates based on low tagging mortality among size-groups were reduced, except for groups 90119 and 180+ which still varied by a factor of two. In contra st, MLE estimates applying high tagging mortality exhibited large va riation among length-groups where q varied by a factor greater than 2 when comparing the 90-119 group to groups 120-149, 150-179 and a factor of 4 to group 180+. Overall model trends indicated decreasi ng catchability to trawl gear as fish length increased, with a greater amount of uncertainty in the estimates for the smallest length group (90119). Indirect Measure of Selectivity Black crappie growth varied among lakes (F igure 7). Average asymptotic length ( L) among lakes varied from 335 to 398, whereas metabolic growth coefficient (K ) ranged from 0.27 to 0.42, and t0 from -0.79 to -1.17. As expected lakes with higher K values had lower L values, and vice versa (Figure 7). The general patterns observed in the catch-at-age data include d higher catches of age-0 and age-1 fish relative to older age classes, as w ould be expected for any population. Age-0 catch rates among the lakes varied from 0.018 to 16.84 with an average of 3.24 (fish/min). Age-1 catch at age ranged from 0.016 to 10.83 with a m ean of 2.12, whereas age-2 CPUE varied from 0.007 to 7.45 and averaged of 0.94 (fish/min). Catch rates for crappie 3 and older were considerably less relative to younge r age-classes and ranged from 0 to 3.70 with average catch rates to 0.37 (Table 2). Relative recruitment anomalies varied by lake an d some lakes exhibited large fluctuation in recruitment while others showed little variabil ity in recruitment strength (Figure 8). The 26

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recruitment anomalies for Lake Griffin varied fro m 0.24 to 3.57 with an average value of 1.13. Lake Johns recruitment values varied from 0.85 to 1.12 with a mean rela tive recruitment of 0.99 indicating little variability in recruitment. Lake Lochloosa anomalies ranged from 0.14 to 2.35 with an average recruitment va lue of 1.04. Lake Okeechobee exhibited large fluctuations in year-class strength with recruitmen t values ranging from 0.01 to 5.01 with a mean of 1.50. Thus, the recruitment trends as indexed with age-0 fish catch rates suggested su bstantial variation in recruitment among years at each lake. My age structured model estimated dome-shape d selectivity with peak values for black crappie in bottom trawls at age-1. In general, age-0 and age-1 fish were more susceptible to trawl gears than older age-cl asses (ages-2+). Model simulations for the base model ( Zo = 1.2 = So = 0.30, Z = 0.6 = S = 0.55) revealed peak se lectivity at age-1 for all lakes (Figure 9). The average selectivity schedule for Lakes Griffin, John, and Lochl oosa sampled with the standard bottom trawl gear also i ndicated peak selectivity at age-1 (Figure 10). Varying assumptions for instantaneous rates of mortality (i.e. survival) influenced the selectivity parameter estimates. When survival to age-1 increased (lower Zo) greater numbers of older fish were available for capture decreasing the corresponding proportion of age-0 fish in the catch, thus increasing selectivity estimates for age-0 fish (Figure 11). Under this scenario, all selectivity schedules peaked at age-1 as before, but selectivity estimates for age-0 fish increased. Lake Johns was the only exception which exhibited peak selectivity at age-0 declining with age if survival to age-1 increased (Figure 11). Conve rsely, when survival to age-1 decreased (higher Zo) fewer numbers of older age-classes were availa ble for capture, decrea sing their proportion in the catch. The corresponding propo rtion of age-0 fish in the catch increased resulting in lower age-0 selectivity estimates. When su rvival past age-1 increased (lower Z ), greater numbers of 2+ 27

PAGE 28

28 age-class fish were available for capture which re sulted in increased proportions of older fish in the catch. These increased proportions of older age-classes represented in the catch resulted in decreased selectivity estimates for those ages. If survival past age-1 decreased (higher Z ) fewer numbers of 2+ age-class fish became available for capture. Under this scenario, decreased proportions of older fish resulted in increased selectiv ity estimates. Overall, the selectivity estimates for the older age-classes were more sensitive to changes in survival compared with age-0 and age-1 estimates (Figure 11). For example, Lake Okeechobee results indicated the selectivity estimates for the olde r age-classes could vary by a factor of 2 or 3 from the base model estimates. Nevertheless, changes in assu med survival rates did not change the overall pattern of dome-shaped selectivity for bo ttom trawls (Figure 11). The uncertainty in the age-specific selectivity estimates are described from probability profiles (similar to p-values) for each lake in Figures 12 15. Most age-specific selectivity profiles indicated wide probability bands with pot ential selectivity estimates ranging from 0 to 1 for most lakes. Lake Okeechobee estimates exhi bited tighter intervals relative to other lake selectivity estimates. This is likely attributed to a longer timeseries of data (model fit to 10 years), whereas other lakes had shorter data tim e-series resulting in wider probability bands. Age-1 selectivity exhibited tighter intervals ( on average from 0.70 to 1) than all other agegroups.

Figure 3-13. Lake Johns probability profiles with relative selectivity on the x-axis and relative probability on the y-axis. 43

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Figure 3-14. Lake Lochloosa proba bility profiles with relative selectivity on the x-axis and relative probability on the y-axis. 44

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45 Figure 3-15. Lake Okeechobee probability profiles with relative selectivity on the x-axis and relative probability on the y-axis.

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CHAPTER 4 DISCUSSION Bottom trawls are an extremely efficient captur e gear (reflected by large catches of various aquatic organisms and use in many fisheries worl dwide), but often select for small finfish and crustaceans (Kjelson and Johnson 1978; Rulifson et al. 1992; Diamond et al. 1999). The size selectivity exhibited by bottom trawls results in large catches of small aquatic organisms (as evident by their use in commercial shrimp fisheries) and the inci dental catch of commercially and recreationally important j uvenile fishes (Howell and La ngan 1992; Gallaway and Cole 1999; Diamond et al. 1999; Wakefi eld et al. 2007). My direct and i ndirect estimates of bottom trawl selectivity corroborated one another and indicated decr easing size selectiv ity with increasing length, suggesting that bottom tr awls would be best for monitoring the abundance of small black crappie but may be inadequate to characterize the adult population. Catchability of black crappie has seldom been measured, but Miranda and Dorr (2000) found q with angling gear to vary with fish size. Furthermore, Mc Inerny and Cross (2006) found q with trap-nets varied with size, season, and density and used these estimates to im prove the interpretation of CPUE data to index abundance and describe size stru ctures. My estimates of q lend insight into the size selective properties of bottom trawls for black crappie, and could be incorporated w ith catch data to index the true population age/size compos ition (e.g., Lake Jeffords). My age-structured model simulations indicated dome-shaped selectivity to bottom trawls for black crappie and the highest selectivity estimates were for age-1 fish. Dome-shaped selectivity patterns are common for many sample gears and species (E rzini and Castro 1998; Jackson and Noble 1995; Miranda and Dorr 2000; Tanaka 2002), a nd evaluating differences in relative selectivity by age/size allows managers to adju st indices of abundance (Quinn and Deriso 1999) or determine gear efficiencies on th e different age/size classes (Pierce et al. 1994). 46

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Selectivity peaked at age-1 for all lakes under all scenarios except Lake Johns which exhibited decreasing relative selectivity with age when assumed age-0 survival was high. The results corroborate other selectivity st udies (Jagielo 1999; Bayley and Austen 2002; McInerny and Cross 2006) and indicate the trawl gear may be most useful for tracking small-sized black crappie through time. Selectivity estimates exhi bited high uncertainty ex cept for age-1, but the observed selectivity patterns were similar across la kes. Individual selectivity estimates from my analyses should be viewed with caution, but the overall pattern of dome-shaped selectivity probably describes the general pattern of bo ttom trawl selectivity for black crappie. Gear selectivity determines the effect of fishing on size/age structures. As such, assessment models can link size/age composition of catch data to size/age composition of the fishery (Taylor et al. 2005) to predict the effect s of different harvest ra tes, calculate biological reference points, and identify maximum sustaina ble yields (Maunder 2002). Assuming constant catchability among size/age classes is a dangerous assumption and can lead to bias in yield models (Ricker 1975) because differe nces in age/size specific catchability to fishing gears can alter the maximum sustainable yield (MSY) obt ainable from a fishery (Maunder 2002). When fishing mortality is restricted th rough effort controls, the catchab ility coefficient becomes a vital parameter in yield models, due to the relationship between q and yield and abundance. Gear selectivity patterns and the cumulative eff ects of size-selective fishing practices often produce bias in length at age sa mples (Sinclair et al. 2002, Taylor et al. 2005). In exploited populations, fast growing young fish and slow growing older fish ar e overrepresented in length at age samples. In turn, this biases the parame ter estimates of growth models used to describe mean length at age. My model results indi cated bottom trawl selectivity for younger size/ageclasses. Furthermore, all lakes in the study experience some level of angler exploitation. Thus, 47

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since trawls appear more effec tive at capturing small fish and th e older age-classes are exposed to harvest, growth models from trawl data co llected from exploited populations may severely underestimate the asymptotic length ( L) and overestimate the metabo lic growth coefficient ( K ). These inaccuracies can lead to biased mean length at age which would influence estimates of maximum yield and optimal harvest policies. Age/size selectivity of sampling gears cause s errors in the estimation of population structure (Walters and Hilborn 1992) and may limit the ability to dr aw inferences about trends in abundance. Therefore determini ng size selectivity of fishery i ndependent assessment gears is important when survey CPUE data are used to index abundance, especially if assessment gears may not adequately sample the ag e and size range targeted by the fishery. For example, I found that large adult black crappies we re poorly represented in bottom trawl samples, but these fish are targeted by recreational fisher ies. Thus, bottom trawls may not detect changes in abundance even if large fish suffer high fish ing mortality. This could lead to appearances of hyperstability (i.e., relatively constant CPUE over a large range of true fish abundance) because trawl catches of large black crappie could potentially be low and not change widely with changes in abundance. When properly designed and implemented, mark-recapture studies can provide managers with information on growth, mortal ity, and reproduction, population size and structure, and gear selectivity (Seber 1982). Data fo r closed capture-recapture methods must meet several criteria. Foremost, the population of interest must be cl osed to additions and deletions. That is, recruitment, natural mortality, immigration, a nd emigration must be minimal. The immigration and emigration assumptions were probably not violat ed in my study due to the closed system and short time interval over which the study occurred. The mortality assumption was violated due to 48

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tagging mortality but I adjusted for this in my calculations. Another assumption for mark recapture experiments is that marks are not lost or undetected by the recorder. Tag retention and detectability were likely very hi gh in my study because I marked fi sh with a pelvic fin clip and sampled over a short interval befo re regeneration of fins could o ccur. A third requirement for closed mark recapture experiments is equal cap ture probability (i.e. no capture heterogeneity and/or trap response). Trap response is when capture probability is dependent on an animals capture history, and is difficult to directly estimate (Pollock et al 1990). I estimated heterogeneity in capture probability as a func tion of fish size and account for potential trap responses by using different gear types for mark and recap events. One potential cause of bias and uncertainty in q estimates from the mark-recapture experiment could be the higher observed sampli ng mortality for the sm aller length-groups (90119, 120-149) relative to the larger groups (150179, 180+). This size selective mortality significantly reduced the number of fish availa ble for recapture among those two groups. An overestimation in tagging mort ality for these groups would cause a positive bias in my q estimates, resulting in more of an effect on trawl size selectiv ity. Alternately, underestimating sampling mortality could have caused a negative bi as in the catchability estimates. The larger observed mortality on the smaller size groups could have resulted from high handling times and greater sampling stress on these classes relative to the larger length groups. The likelihood profiles also show there is mo re uncertainty in the likeli hood estimate for the 90-119 size-group when compared to other groups. The larger variance can likely be attributed to the higher observed sampling mortality for this group. In tu rn, this influenced the number available for recapture (smaller N for this group) leading to a greater difference between observed and expected recaptures. 49

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Differences in growth among the lakes could expl ain some of the differences in selectivity at age between lakes. Some lakes appeared to exhibit faster growth, which could have resulted in fewer numbers of faster growi ng individuals in the catch (as de scribed above). This may have resulted in biased mean length at age for some lakes which may explain some of the observed differences in age-specific selectivities across systems. However, these differences appeared minimal because the overall pattern of dome-s haped selectivity occurred at all lakes. My approximation of age-length keys using prev ious and post year data at Lake Lochloosa could pose problems in the estimated age structur e and thus, the catch ra te indices for those years. Inaccuracies in observed catch-at-age could have aff ected my proportions of catch-at-age and model fit having the potential to cause unknown biases in the selectivity estimates. However, selectivity patterns for Lake Lochloos a were similar to those observed at the other lakes where age data were collected every year. Thus, I believe the selectivity patterns at Lake Lochloosa reflected real differen ces in across age-classes despite the gaps in the age structure data. Furthermore, variation in mort ality at size/age can affect the numbers in each age class available for capture leading to bi ased selectivity estimates. I a ccounted for this variability in survival to age by apportioning mortality into two classes (surviva l to age-1 and survival past age-1) based on life stanzas. I also simulated vari ous levels of survival to show how different rates of survival on the age-cla sses could influence the numbers available for capture and thus, my selectivity estimates. I did not attempt to quantify exploitation rates on fish vulnerable to angling (>= age-2) which may have decreased survival for some of the older age-classes relative to younger age-classes. If exploitation is high on older age-cla sses, the corresponding proportions of catch in those age-classes would decrease resulting in an increase in the younger 50

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age-class proportions and an upward bias in the selectivity estimate s for older/larger fish and a downward bias in selectivity es timates of younger/smaller fish. I assumed constant survival among years and did not evaluate the effects of ch anges in mortality between years. Changes in yearly natural and/or fishing mortality could significantly increase or reduce the numbers available for capture creating both positive and negative biases in selectivity estimates. The indirect approach I used to estimate tr awl selectivity is common data collected for recreational and/or commercially important fish eries. This methodol ogy is quite common in marine assessments (Walters and Martell 2004) but examples are lacking for freshwater fisheries applications. I found this approach quite useful for estimating gear selectivity and recommend its application when catch-at-age time series data are available and when direct estimates are infeasible (e .g., large, open systems). Use of my selectivity estimates should be rest ricted to similar seasons and lake types as used in this study. Size-selec tivity of bottom trawls was evalua ted during fall for the indirect method and winter for the direct method. Thus, the estimates provided may not apply to other seasons or times of the year. Seasonal effects on fish behavior are well documented (Pope and Willis 1996, Hayes et al. 1996) and sampling black crappie with bottom trawls at other times in the year may result in unknown bias in the CPUE index. Use of my sel ectivity estimates should be restricted to similar seasons from which they were derive d. Lake characteristics and differences in geomorphology may have influenced my results. For example, gear efficiency and thus, catchability may vary due to differences in amount of suitable habitat, percent area coverage of macrophytes (PAC), depth, dissolved oxygen (DO) levels, substrate type or a variety of other factors. I did not atte mpt to quantify all of these effect s and managers should be aware of these potential sources of bias and use the selectivity estimates with prudence. Furthermore, 51

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systems selected for trawl sampling should be de termined carefully to maximize efficiency and prevent gear fouling. Bottom trawl sampling may be an inappropriate gear on some lakes due to excessive bottom debris. Submerged debris, su ch as stumps or large amounts of aquatic macrophytyes may prevent effective trawl sampling limiti ng sampling and/or gear efficiency. The likely cause for size selectivity of smaller black crappie to otter trawls may be the ability of larger fish to detect or avoid the gear. Net avoidance and escapement after initial capture could result since swimmi ng speed tends to increase with fish length (Helfman et al. 1997). Furthermore, gear avoidance by the larger fi sh may result due to the trawl pressure wake. This pressure wake may be detected due to larger and more developed lateral lines in larger fish. Spatial distributional patterns and habitat availa bility and use may also be a source for trawl selectivity. The smaller fish may utilize the pelagic zones of a lake and school more relative to large fish, where the larger fish may be more patchily distributed utilizing both open water and littoral areas of lakes. However, we controlled for differences in spatia l distribution and habitat availability/use in the direct measure at Lake Jeffords, where crappie of all size ranges and the majority of fish were captured in open water habitats. Differences in spatial distribution patterns may have had more of an influence on the indire ct selectivity estimates where there is likely a greater difference in habitat av ailability and use due to lake size and habitat complexity. However, the direct measure of selectivity suggests that the ability of larger fish to detect/avoid the gear is the likely source for trawl selectivity of younger/smaller crappie. Stock assessments are important for fisherie s management and attempt to recreate past stock trends to explain current stock trends and abundance by making quantitative predictions about the reactions of fish popul ations to alte rnative management options (Hilborn and Walters 1992). Therefore, bottom trawl catch data may be important for stock assessment analysis and 52

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53 used for indices of recruitment, as measures of recruitment are one of the important inputs used for stock assessments. These data could be used in conjunction with another gear type that indexes the adult population (such as a creel) along with an es timate of fishing mortality to evaluate current stock trends facilitating the ability to make choices among policy options and evaluate the trade-offs associat ed with those decisions. Otter trawls have been show n to effectively capture young black crappies (Allen et al. 1999, Pine 2000), and my results suggest that botto m trawls provided adequa te catches of age-0 and age-1 fish. As such, otter trawls are probably most effec tive at tracking age-0 and age-1 abundances and can be used for estimates of yea r-class strength. However, my results suggested that the trawls may be inadequa te to describe the adult populati on, fish growth rates, and age structure estimates due to substantially lower sele ctivity values for large fish. The selectivity estimates provided will allow managers to adjust abundance indice s and correct age/size structures for relatively shallow Florida lakes.

BIOGRAPHICAL SKETCH Gregory Robert Binion was born on September 18, 1978, at a U.S. air force base in the United Kingdom, to Mike and Peggy Binion. Shortly after, he moved and was raised in San Antonio, Texas, with his older brother Pete. At a young age, he developed a passion for the outdoors and enjoyed much of his time exploring the wide open spaces and beautiful country of South Texas. He graduated from the University of Kentucky with a B.A. in political science in December 2002. Shortly after graduation, he re located to Florida to pursue an interest in fisheries biology and management. In October 2003, he began to work as a fisheries technician on various projects at the Universi ty of Florida and began his gradua te work at the Department of Fisheries and Aquatic Sciences at the University of Florida in January 2006.He will graduate with a Master of Science in December 2007. Hi s future plans include traveling, passing time fishing and hunting, spending time with his family, and pursuing a ca reer in fisheries management. 60